segmentation

Using this open-source model in production? Consider switching to pyannoteAI for better and faster options. 🎹 Speaker segmentation Paper | Demo | Blog post Usage Relies on pyannote.audio 2.1.1: see installation instructions. 1. visit hf.co/pyannote/segmentation and accept user conditions 2. visit hf.co/settings/tokens to create an access token 3. instantiate pretrained model from pyannote.audio import Model model = Model.from_pretrained("pyannote/segmentation", use_auth_token="ACCESS_TOKEN_GOES_HERE") Voice activity detection from pyannote.audio.pipelines import VoiceActivityDetection pipeline = VoiceActivityDetection(segmentation=model) HYPER_PARAMETERS = { onset/offset activation thresholds "onset": 0.5, "offset": 0.5, remove speech regions shorter than that many seconds. "min_duration_on": 0.0, fill non-speech regions shorter than that many seconds. "min_duration_off": 0.0 } pipeline.instantiate(HYPER_PARAMETERS) vad = pipeline("audio.wav") `vad` is a pyannote.core.Annotation instance containing speech regions Overlapped speech detection from pyannote.audio.pipelines import OverlappedSpeechDetection pipeline = OverlappedSpeechDetection(segmentation=model) pipeline.instantiate(HYPER_PARAMETERS) osd = pipeline("audio.wav") `osd` is a pyannote.core.Annotation instance containing overlapped speech regions Resegmentation from pyannote.audio.pipelines import Resegmentation pipeline = Resegmentation(segmentation=model, diarization="baseline") pipeline.instantiate(HYPER_PARAMETERS) resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline}) where `baseline` should be provided as a pyannote.core.Annotation instance Raw scores from pyannote.audio import Inference inference = Inference(model) segmentation = inference("audio.wav") `segmentation` is a pyannote.core.SlidingWindowFeature instance containing raw segmentation scores like the one pictured above (output) Citation @inproceedings{Bredin2021, Title = {{End-to-end speaker segmentation for overlap-awar